Lessons Learned From Canada in Working Together to Support Indigenous Health and Wellness
Bibliographic record
Abstract
Background and context: First Nations, Inuit and Métis bear a disproportionate burden of cancer in Canada. In the spirit of truth and reconciliation, and to have the greatest impact, it is important for nonindigenous and indigenous partners to work together, and reflect on lessons learned in collaborating, to support First Nations, Inuit and Métis health and wellness. Aim: In response to the national Truth and Reconciliation Commission Calls to Action, the Canadian Partnership Against Cancer committed to understanding how collaborative projects funded through the Coalitions Linking Action and Science for Prevention (CLASP) initiative were successful in bringing together diverse groups - both indigenous and nonindigenous - to create and apply culturally-relevant cancer prevention approaches. Strategy/Tactics: Seven projects funded through the CLASP initiative, from 2009 to 2016, brought together over 275 First Nations, Inuit, or Métis communities, schools, and organizations with government, nongovernment, and academic partners in collaborative coalitions. The projects addressed cancer prevention issues prioritized by First Nations, Inuit, and Métis (e.g., unhealthy eating and physical inactivity) through approaches that were holistic and culturally-relevant, such as utilizing intergenerational knowledge sharing, incorporating mental wellness, and supporting existing capacity within communities. Program/Policy process: Over 30 knowledge products developed by the projects were reviewed to identify preliminary lessons learned about partner collaboration. Preliminary lessons learned were verified and expanded upon through nine key informant interviews with CLASP partners. Key informant interviews were informed by four advisors representing indigenous and nonindigenous leaders and partners. The refined set of lessons learned were finalized through qualitative analysis and validated through a conference session and one-day workshop with CLASP partners and First Nations, Inuit, and Métis community leaders. Outcomes: Twenty-seven lessons learned that describe how nonindigenous and First Nations, Inuit and Métis CLASP partners worked together to develop and put into practice culturally-appropriate cancer prevention approaches were identified. The lessons learned were grouped into six themes: 1. respectful relationships; 2. engagement with indigenous communities; 3. addressing accountability requirements, decision-making, and governance; 4. community direction; 5. supports and resources; 6. communication and knowledge exchange. What was learned: The actionable lessons learned are intended to guide future relationship building and engagement between nonindigenous partners and First Nations, Inuit and Métis partners. It is intended that these lessons will be beneficial to collaborative cancer prevention efforts around the world and inform broader system change leading to a reduction in indigenous cancer burden disparities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.049 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".